PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 28, 2026Global Ecology and Conservation2 citationsOpen Access

Predicted habitat shifts and conservation priorities for climate-sensitive fish in mountain rivers across a climatic transition zone under future climate change

View Full Paper
YZYuchen ZhengBPBaozhu PanXLXing Liu

Key Points

  • The study aims to assess habitat shifts and conservation priorities for climate-sensitive fish in mountainous river systems under climate change.
  • Developed a climate-responsive evaluation framework (CR-TOPSIS) based on TOPSIS.
  • Integrated environmental DNA (eDNA) surveys and traditional capture methods.
  • Applied MaxEnt modeling to predict future habitat changes under two climate scenarios (SSP126 and SSP585).
  • Conducted assessments of fish species distributions in the Qinba mountainous region.
  • Identified pronounced asymmetric habitat shift patterns, with leading-edge expansion outpacing trailing-edge contraction.
  • Forecasted northward shifts in species distribution centroids, averaging 1.76 km under SSP126 and 2.64 km under SSP585.
  • Coldwater and benthic species showed significant habitat loss under high-emission scenarios, while eurythermal species demonstrated higher adaptability.
  • Mapped climate refugia of 0.51 × 10³ km² along the midstream Hanjiang River, stable across climate scenarios.

Abstract

Identifying suitable habitat shifts and climate refugia under climate change is essential for the conservation of freshwater fish, particularly in mountainous river systems. However, there is a lack of comprehensive evaluation frameworks to quantitatively assess the degree to which regional fish assemblages are affected. In addition, asymmetric habitat shift patterns in fish populations of mountainous rivers remain insufficiently explored. To address these gaps, we developed a climate-responsive evaluation framework—CR-TOPSIS—based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and applied it to assess current and future habitat changes for the Top 15 climate-sensitive species and other key protected species in the Qinba mountainous region, integrating environmental DNA surveys and targeted traditional capture, cross-validated against regional checklists and expert review with MaxEnt modeling. Projections were made under two climate scenarios (SSP126 and SSP585) for the period 2070–2100. Results indicated pronounced asymmetric habitat shift patterns, with leading-edge expansion consistently exceeding trailing-edge contraction across climate scenarios. Species distribution centroids exhibited a clear northward shift, averaging 1.76 km under SSP126 and 2.64 km under SSP585, reflecting enhanced redistribution under stronger warming. Coldwater and bottom-dwelling species experienced disproportionate habitat loss under high-emission scenarios, whereas eurythermal and pelagic-spawning species showed comparatively higher adaptive potential. Core climate refugia, defined by 100% spatial overlap across scenarios, covered approximately 0.51 × 10³ km² and were primarily concentrated along the midstream Hanjiang River and its tributaries, remaining stable under both climate pathways. This study demonstrates the utility of integrating molecular monitoring and species distribution models to detect climate-sensitive shifts, evaluate species vulnerability and conservation prioritization in montane freshwater ecosystems. • Developed a CR-TOPSIS framework to evaluate climate sensitivity of freshwater fish species. • Integrated eDNA data and MaxEnt modeling to predict future habitat suitability under SSP126 and SSP585. • Revealed consistent asymmetric habitat shifts, with leading-edge expansion exceeding trailing-edge retreat. • Identified coldwater and benthic species as highly vulnerable to warming and hydrological change. • Mapped climate refugia concentrated in the midstream Hanjiang River, providing conservation targets.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/69a287570a974eb0d3c02f0ehttps://doi.org/10.1016/j.gecco.2026.e04132
Ask AI
Helpful
Bookmark
Share
View Full Paper